All Questions
12 questions
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575
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Why does my Deep Learning Network converge to mean value of series when trying to fitting & predicting a time series?
There's a task requiring me to predict selling price of a product with provided external data containing historical purchasing prices and selling prices of competitors(positive correlated). ...
CommunityBot
- 1
1
vote
1
answer
395
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LSTM: How Do I Predict A Single Label Multiple Steps Ahead?
I'm building an LSTM neural network using the Tensorflow tutorial below.
https://www.tensorflow.org/tutorials/structured_data/time_series#single-shot_models
It shows you how to build single-step ...
CommunityBot
- 1
1
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0
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97
views
Can vanilla NN model a moving Average (MA) process?
Let say I simulate an AR(1) process. We can easily model the data with a vanilla NN with a single neuron as it is about finding the linear relationship between $y$ and $y_{t-1}$.
Now, how about a MA(1)...
1
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1
answer
54
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Low memory time series input for deep learning
Background
I have some data that looks like this:
...
CommunityBot
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1
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1
answer
346
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Time series analysis of hybrid data with RNN?
I have a time series $x_t\in\mathbb{R}^n$ where $t=1,\dots T$. For some $t$, I have also a message which is a piece of text, i.e. sequence of characters of unspecified length. I assume that the ...
- 82.8k
0
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1
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1k
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Regression for noisy data with tensorflow: low train and validation errors but high test error
I have a training set of 6400 samples. Each sample is composed of an input of size 100, which is essentially a noise process. The input of the first sample is:
The output is the solution of a ...
- 143
1
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0
answers
176
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autoencoding spiky time series - better loss function?
I am experimenting with convolutional autoencoders for time series. My first network architectures work quite well. However, the autoencoder has a tendency to soften the spikes in the time series. And ...
- 253
1
vote
1
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79
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How to handle timeseries extremes (sigma > 20) in deep learning?
I'm using 16-channel, 400-Hz, standardized EEG data to train CNN-LSTM for seizure classification. The data contains $O(3)$ sigma > 20 points, rarely thousands in a ...
- 21.4k
3
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1
answer
10k
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How to deal with really sparse time series data for a binary classification task using RNN or LSTM?
I have a binary classification prediction task and more often than not, the time series data is like really sparse. The number of zeroes in the time series data is almost always more than 99%. I ...
- 12.1k
1
vote
1
answer
46
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Which algorithm for classification problem?
I want to create a ML (DL) model, that predicts the success of Facebook page-posts, based on historical data.
My dataset represents a couple thousands posts, labeled 1 (successful) and 0 (...
- 5,257
2
votes
1
answer
2k
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Univariate time series multi step ahead prediction using multi-layer-perceptron (MLP)
I have a univariate time series data. I want to do a multi-step prediction. I came across this question which explains time series one step prediction.
but I am interested in multi-step ahead ...
- 5,800
0
votes
1
answer
785
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When to use ANN with tensorflow?
I'm new to machine learning and tensorflow and I'm confused as to why (and when) to use the types of ANN (ie recurrent neural network) with tensorflow? I know RNN is good for sequences of data/time ...
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